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A New Graph Drawing Scheme for Social Network

With the development of social networks, people have started to use social network tools to record their life and work more and more frequently. How to analyze social networks to explore potential characteristics and trend of social events has been a hot research topic. In order to analyze it effect...

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Detalles Bibliográficos
Autores principales: Wang, Eric Ke, Zou, Futai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124209/
https://www.ncbi.nlm.nih.gov/pubmed/25157378
http://dx.doi.org/10.1155/2014/930314
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author Wang, Eric Ke
Zou, Futai
author_facet Wang, Eric Ke
Zou, Futai
author_sort Wang, Eric Ke
collection PubMed
description With the development of social networks, people have started to use social network tools to record their life and work more and more frequently. How to analyze social networks to explore potential characteristics and trend of social events has been a hot research topic. In order to analyze it effectively, a kind of techniques called information visualization is employed to extract the potential information from the large scale of social network data and present the information briefly as visualized graphs. In the process of information visualization, graph drawing is a crucial part. In this paper, we study the graph layout algorithms and propose a new graph drawing scheme combining multilevel and single-level drawing approaches, including the graph division method based on communities and refining approach based on partitioning strategy. Besides, we compare the effectiveness of our scheme and FM(3) in experiments. The experiment results show that our scheme can achieve a clearer diagram and effectively extract the community structure of the social network to be applied to drawing schemes.
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spelling pubmed-41242092014-08-25 A New Graph Drawing Scheme for Social Network Wang, Eric Ke Zou, Futai ScientificWorldJournal Research Article With the development of social networks, people have started to use social network tools to record their life and work more and more frequently. How to analyze social networks to explore potential characteristics and trend of social events has been a hot research topic. In order to analyze it effectively, a kind of techniques called information visualization is employed to extract the potential information from the large scale of social network data and present the information briefly as visualized graphs. In the process of information visualization, graph drawing is a crucial part. In this paper, we study the graph layout algorithms and propose a new graph drawing scheme combining multilevel and single-level drawing approaches, including the graph division method based on communities and refining approach based on partitioning strategy. Besides, we compare the effectiveness of our scheme and FM(3) in experiments. The experiment results show that our scheme can achieve a clearer diagram and effectively extract the community structure of the social network to be applied to drawing schemes. Hindawi Publishing Corporation 2014 2014-07-16 /pmc/articles/PMC4124209/ /pubmed/25157378 http://dx.doi.org/10.1155/2014/930314 Text en Copyright © 2014 E. K. Wang and F. Zou. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Eric Ke
Zou, Futai
A New Graph Drawing Scheme for Social Network
title A New Graph Drawing Scheme for Social Network
title_full A New Graph Drawing Scheme for Social Network
title_fullStr A New Graph Drawing Scheme for Social Network
title_full_unstemmed A New Graph Drawing Scheme for Social Network
title_short A New Graph Drawing Scheme for Social Network
title_sort new graph drawing scheme for social network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124209/
https://www.ncbi.nlm.nih.gov/pubmed/25157378
http://dx.doi.org/10.1155/2014/930314
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